This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the message queue telemetry transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) system developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise; fear; happiness; sadness; disgust; anger; or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the Message Queue Telemetry Transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) System developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise, fear, happiness, sadness, disgust, anger or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

Background Despite representing the largest fraction of animal life, the number of insect species whose genome has been sequenced is barely in the hundreds. The order Dermaptera (the earwigs) suffers from a lack of genomic information despite its unique position as one of the basally derived insect groups and its importance in agroecosystems. As part of a national educational and outreach program in genomics, a plan was formulated to engage the participation of high school students in a genome sequencing project. Students from twelve schools across Chile were instructed to capture earwig specimens in their geographical area, to identify them and to provide material for genome sequencing to be carried out by themselves in their schools. Results The school students collected specimens from two cosmopolitan earwig species: Euborellia annulipes (Fam. Anisolabididae) and Forficula auricularia (Fam. Forficulidae). Genomic DNA was extracted and, with the help of scientific teams that traveled to the schools, was sequenced using nanopore sequencers. The sequence data obtained for both species was assembled and annotated. We obtained genome sizes of 1.18 Gb (F. auricularia) and 0.94 Gb (E. annulipes) with the number of predicted protein coding genes being 31,800 and 40,000, respectively. Our analysis showed that we were able to capture a high percentage (≥ 93%) of conserved proteins indicating genomes that are useful for comparative and functional analysis. We were also able to characterize structural elements such as repetitive sequences and non-coding RNA genes. Finally, functional categories of genes that are overrepresented in each species suggest important differences in the process underlying the formation of germ cells, and modes of reproduction between them, features that are one of the distinguishing biological properties that characterize these two distant families of Dermaptera. Conclusions This work represents an unprecedented instance where the scientific and lay community have come together to collaborate in a genome sequencing project. The versatility and accessibility of nanopore sequencers was key to the success of the initiative. We were able to obtain full genome sequences of two important and widely distributed species of insects which had not been analyzed at this level previously. The data made available by the project should illuminate future studies on the Dermaptera.